What is an "adversarial attack"
"Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines.".


Source: "Attacking Machine Learning with Adversarial Examples"

You create an input and test against the output, tuning the input to maximize the error. There are different criteria for tuning the input, sometimes you might want the shortest possible input to create the largest error, often you would want very similar input to cause the greatest error.

Example: I take a \$1 bill and write a few more zeros after the one; you accept it as a \$1000 bill.